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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code>.BayesianRidge</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-linear-model-bayesianridge">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.linear_model.BayesianRidge</span></code></a></li>
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  <div class="section" id="sklearn-linear-model-bayesianridge">
<h1><a class="reference internal" href="../classes.html#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a>.BayesianRidge<a class="headerlink" href="#sklearn-linear-model-bayesianridge" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.linear_model.BayesianRidge">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.linear_model.</code><code class="sig-name descname">BayesianRidge</code><span class="sig-paren">(</span><em class="sig-param">n_iter=300</em>, <em class="sig-param">tol=0.001</em>, <em class="sig-param">alpha_1=1e-06</em>, <em class="sig-param">alpha_2=1e-06</em>, <em class="sig-param">lambda_1=1e-06</em>, <em class="sig-param">lambda_2=1e-06</em>, <em class="sig-param">alpha_init=None</em>, <em class="sig-param">lambda_init=None</em>, <em class="sig-param">compute_score=False</em>, <em class="sig-param">fit_intercept=True</em>, <em class="sig-param">normalize=False</em>, <em class="sig-param">copy_X=True</em>, <em class="sig-param">verbose=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_bayes.py#L23"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge" title="Permalink to this definition">¶</a></dt>
<dd><p>Bayesian ridge regression.</p>
<p>Fit a Bayesian ridge model. See the Notes section for details on this
implementation and the optimization of the regularization parameters
lambda (precision of the weights) and alpha (precision of the noise).</p>
<p>Read more in the <a class="reference internal" href="../linear_model.html#bayesian-regression"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_iter</strong><span class="classifier">int, default=300</span></dt><dd><p>Maximum number of iterations. Should be greater than or equal to 1.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-3</span></dt><dd><p>Stop the algorithm if w has converged.</p>
</dd>
<dt><strong>alpha_1</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>Hyper-parameter : shape parameter for the Gamma distribution prior
over the alpha parameter.</p>
</dd>
<dt><strong>alpha_2</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the alpha parameter.</p>
</dd>
<dt><strong>lambda_1</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>Hyper-parameter : shape parameter for the Gamma distribution prior
over the lambda parameter.</p>
</dd>
<dt><strong>lambda_2</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the lambda parameter.</p>
</dd>
<dt><strong>alpha_init</strong><span class="classifier">float, default=None</span></dt><dd><p>Initial value for alpha (precision of the noise).
If not set, alpha_init is 1/Var(y).</p>
<blockquote>
<div><div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</div></blockquote>
</dd>
<dt><strong>lambda_init</strong><span class="classifier">float, default=None</span></dt><dd><p>Initial value for lambda (precision of the weights).
If not set, lambda_init is 1.</p>
<blockquote>
<div><div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</div></blockquote>
</dd>
<dt><strong>compute_score</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, compute the log marginal likelihood at each iteration of the
optimization.</p>
</dd>
<dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to calculate the intercept for this model.
The intercept is not treated as a probabilistic parameter
and thus has no associated variance. If set
to False, no intercept will be used in calculations
(i.e. data is expected to be centered).</p>
</dd>
<dt><strong>normalize</strong><span class="classifier">bool, default=False</span></dt><dd><p>This parameter is ignored when <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code> is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
<a class="reference internal" href="sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.preprocessing.StandardScaler</span></code></a> before calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>
on an estimator with <code class="docutils literal notranslate"><span class="pre">normalize=False</span></code>.</p>
</dd>
<dt><strong>copy_X</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, X will be copied; else, it may be overwritten.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, default=False</span></dt><dd><p>Verbose mode when fitting the model.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>coef_</strong><span class="classifier">array-like of shape (n_features,)</span></dt><dd><p>Coefficients of the regression model (mean of distribution)</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">float</span></dt><dd><p>Independent term in decision function. Set to 0.0 if
<code class="docutils literal notranslate"><span class="pre">fit_intercept</span> <span class="pre">=</span> <span class="pre">False</span></code>.</p>
</dd>
<dt><strong>alpha_</strong><span class="classifier">float</span></dt><dd><p>Estimated precision of the noise.</p>
</dd>
<dt><strong>lambda_</strong><span class="classifier">float</span></dt><dd><p>Estimated precision of the weights.</p>
</dd>
<dt><strong>sigma_</strong><span class="classifier">array-like of shape (n_features, n_features)</span></dt><dd><p>Estimated variance-covariance matrix of the weights</p>
</dd>
<dt><strong>scores_</strong><span class="classifier">array-like of shape (n_iter_+1,)</span></dt><dd><p>If computed_score is True, value of the log marginal likelihood (to be
maximized) at each iteration of the optimization. The array starts
with the value of the log marginal likelihood obtained for the initial
values of alpha and lambda and ends with the value obtained for the
estimated alpha and lambda.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>The actual number of iterations to reach the stopping criterion.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>There exist several strategies to perform Bayesian ridge regression. This
implementation is based on the algorithm described in Appendix A of
(Tipping, 2001) where updates of the regularization parameters are done as
suggested in (MacKay, 1992). Note that according to A New
View of Automatic Relevance Determination (Wipf and Nagarajan, 2008) these
update rules do not guarantee that the marginal likelihood is increasing
between two consecutive iterations of the optimization.</p>
<p class="rubric">References</p>
<p>D. J. C. MacKay, Bayesian Interpolation, Computation and Neural Systems,
Vol. 4, No. 3, 1992.</p>
<p>M. E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine,
Journal of Machine Learning Research, Vol. 1, 2001.</p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">BayesianRidge</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="go">BayesianRidge()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="go">array([1.])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.linear_model.BayesianRidge.fit" title="sklearn.linear_model.BayesianRidge.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Fit the model</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.linear_model.BayesianRidge.get_params" title="sklearn.linear_model.BayesianRidge.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.linear_model.BayesianRidge.predict" title="sklearn.linear_model.BayesianRidge.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X[, return_std])</p></td>
<td><p>Predict using the linear model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.linear_model.BayesianRidge.score" title="sklearn.linear_model.BayesianRidge.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the coefficient of determination R^2 of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.linear_model.BayesianRidge.set_params" title="sklearn.linear_model.BayesianRidge.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.linear_model.BayesianRidge.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_iter=300</em>, <em class="sig-param">tol=0.001</em>, <em class="sig-param">alpha_1=1e-06</em>, <em class="sig-param">alpha_2=1e-06</em>, <em class="sig-param">lambda_1=1e-06</em>, <em class="sig-param">lambda_2=1e-06</em>, <em class="sig-param">alpha_init=None</em>, <em class="sig-param">lambda_init=None</em>, <em class="sig-param">compute_score=False</em>, <em class="sig-param">fit_intercept=True</em>, <em class="sig-param">normalize=False</em>, <em class="sig-param">copy_X=True</em>, <em class="sig-param">verbose=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_bayes.py#L150"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.BayesianRidge.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_bayes.py#L168"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Training data</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Target values. Will be cast to X’s dtype if necessary</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">ndarray of shape (n_samples,), default=None</span></dt><dd><p>Individual weights for each sample</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20: </span>parameter <em>sample_weight</em> support to BayesianRidge.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">returns an instance of self.</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.BayesianRidge.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.BayesianRidge.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">return_std=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_bayes.py#L294"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict using the linear model.</p>
<p>In addition to the mean of the predictive distribution, also its
standard deviation can be returned.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Samples.</p>
</dd>
<dt><strong>return_std</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to return the standard deviation of posterior prediction.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_mean</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Mean of predictive distribution of query points.</p>
</dd>
<dt><strong>y_std</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Standard deviation of predictive distribution of query points.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.BayesianRidge.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
sum of squares ((y_true - y_true.mean()) ** 2).sum().
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a
precomputed kernel matrix or a list of generic objects instead,
shape = (n_samples, n_samples_fitted),
where n_samples_fitted is the number of
samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>R^2 of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The R2 score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor will use
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>. This will influence the
<code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>). To specify the
default value manually and avoid the warning, please either call
<a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a> directly or make a custom scorer with
<a class="reference internal" href="sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> (the built-in scorer <code class="docutils literal notranslate"><span class="pre">'r2'</span></code> uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code>).</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.BayesianRidge.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-linear-model-bayesianridge">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.linear_model.BayesianRidge</span></code><a class="headerlink" href="#examples-using-sklearn-linear-model-bayesianridge" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example compares 2 dimensionality reduction strategies:"><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" src="../../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes a Bayesian Ridge Regression of Sinusoids."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png" src="../../_images/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py"><span class="std std-ref">Curve Fitting with Bayesian Ridge Regression</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes a Bayesian Ridge Regression on a synthetic dataset."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_bayesian_ridge_thumb.png" src="../../_images/sphx_glr_plot_bayesian_ridge_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_bayesian_ridge.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-py"><span class="std std-ref">Bayesian Ridge Regression</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The sklearn.impute.IterativeImputer class is very flexible - it can be used with a variety of e..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
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